DocumentCode :
177009
Title :
Optimal line feature generation from low-level line segments under RANSAC framework
Author :
Li Haifeng ; Chen Rong
Author_Institution :
Coll. of Comput. Sci. & Technol., Civil Aviation Univ. of China, Tianjin, China
fYear :
2014
fDate :
May 31 2014-June 2 2014
Firstpage :
4589
Lastpage :
4593
Abstract :
The low-level line segment features have low accuracy as they are more easily affected by the noise and differeent line segment detectors. Furthermore, the line segment is not a good feature for matching across multiple views when we need to finish the 3D reconstruction. However, the line feature is more robust for the noise. In this paper, a kind of line feature, ideal line, is defined and optimally estimated by clustering the line segments under RANSAC framework. The physical experiments are carried out to verify the proposed estimation method.
Keywords :
image reconstruction; image segmentation; maximum likelihood estimation; 3D reconstruction; RANSAC framework; ideal line; line segment detectors; low-level line segment features; optimal line feature generation; Educational institutions; Feature extraction; Image segmentation; Maximum likelihood estimation; Merging; Noise; Ideal Line; Line Segment; Maximum Likelihood Estimation; RANSAC;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control and Decision Conference (2014 CCDC), The 26th Chinese
Conference_Location :
Changsha
Print_ISBN :
978-1-4799-3707-3
Type :
conf
DOI :
10.1109/CCDC.2014.6852992
Filename :
6852992
Link To Document :
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